EMPLOYEE PERFORMANCE APPRAISAL SYSTEM BASED ON RANKING AND REVIEWS

Objective: In many organizations, employee data have to be maintained and utilized for many purposes. Here, in this paper, we are going to use such data to calculate an employee's performance.Methods: This employee data may be converted into useful information using data mining techniques such as K-means and decisions tree. K-means is used to find the rank of the employee means that the employee may come under in his criteria. Decision tree is used to find the review of an employee means that the employee needs improvement or he/she meets expectation.Results: This algorithm when utilized can identify the top employee who can be considered for appraisal or the eligible candidates for promotion. Hence, these algorithms such as K-mean and decision tree that help to find best employees for any association and help us to take a good decision in less time.Conclusion: There are various factors which should be considered and are limited to this algorithm, so human intervention is required to consider those factors. However, ranking and appraisal are seen in many companies, and this algorithm will definitely identify the potential candidates

A SURVEY ON PRIVACY PRESERVING TECHNIQUES FOR SOCIAL NETWORK DATA

In this era of 20th century, online social network like Facebook, twitter, etc. plays a very important role in everyone's life. Social network data, regarding any individual organization can be published online at any time, in which there is a risk of information leakage of anyone's personal data. So preserving the privacy of individual organizations and companies are needed before data is published online. Therefore the research was carried out in this area for many years and it is still going on. There have been various existing techniques that provide the solutions for preserving privacy to tabular data called as relational data and also social network data represented in graphs. Different techniques exists for tabular data but you can't apply directly to the structured complex graph  data,which consists of vertices represented as individuals and edges representing some kind of connection or relationship between the nodes. Various techniques like K-anonymity, L-diversity, and T-closeness exist to provide privacy to nodes and techniques like edge perturbation, edge randomization are there to provide privacy to edges in social graphs. Development of new techniques by  Integration to exiting techniques like K-anonymity ,edge perturbation, edge randomization, L-Diversity are still going on to provide more privacy to relational data and social network data are ongoingin the best possible manner.Â

Postoperative morbidity following Whipple’s procedure for periampullary carcinoma: a retrospective study spanning 5 years

Background: The morbidity rates for Whipple’s procedure has remained high even as mortality rates were coming down. This study was intended to assess postoperative morbidity rates in  a tertiary care centre and to compare it with other centres.Methods: Data was collected from various registers and medical records for this retrospective cohort study. All Whipple’s procedures for 5 years were included in the study. Statistical analysis was done using R statistical software and the results were tabulated.Results: There were 48 patients and half of them developed morbidity. Surgical site infection was the most common complication (18.8%) followed by pulmonary complications (12.5%) and bile leak (6.25%). Half of the patients having pulmonary complications died while nobody with surgical site infection or bile leak died.Conclusions: In this study the morbidity rates were comparable to other centres. Hypoalbuminemia is a significant predictor of morbidity. Surgical site infection was the most common morbidity. Pulmonary complications were the most common cause for death. Morbidity rate is comparable to other centres and Whipple’s procedure is a safe surgery in the tertiary centre where the study was conducted

A novel medium size lactam ring analoges as antibacterial agents

A novel series of medium size (S)-3-alkyl-3,4,6,7-tetrahydro-1H-benzo[e][1,4]diazonine-2,5-dione (6a-f) analogues were synthesized from (E)-3-(2-nitrophenyl)acrylicacid (2) reacting with various amino acid esters using Di-isopropyl Carbodiimide as a coupling agent. The final cyclization is carried out by using reagent 1-Ethyl-3(3- dimethylaminopropyl) Carbodiimide Hydrochloride. The synthesized compounds have been supported by Mass, 1H-NMR and 13C-NMR. Further antibacterial studies were conducted, where some molecules are noticed with potent activity, especially molecule 6d shown highest activity which was also supported by molecular docking studies. All final molecules were docked with enzyme peptide deformylase to determine the probable binding conformation

Machine Learning Applications in the Neuro ICU: A Solution to Big Data Mayhem?

The neurological ICU (neuro ICU) often suffers from significant limitations due to scarce resource availability for their neurocritical care patients. Neuro ICU patients require frequent neurological evaluations, continuous monitoring of various physiological parameters, frequent imaging, and routine lab testing. This amasses large amounts of data specific to each patient. Neuro ICU teams are often overburdened by the resulting complexity of data for each patient. Machine Learning algorithms (ML), are uniquely capable of interpreting high-dimensional datasets that are too difficult for humans to comprehend. Therefore, the application of ML in the neuro ICU could alleviate the burden of analyzing big datasets for each patient. This review serves to (1) briefly summarize ML and compare the different types of MLs, (2) review recent ML applications to improve neuro ICU management and (3) describe the future implications of ML to neuro ICU management

Impact of Virtual Interprofessional COVID-19 disaster simulation Tabletop Exercise (VICTEr) workshop on Disaster Preparedness among Interprofessional trainees in a tertiary care teaching hospital in India

INTRODUCTION: Disaster planning is of significant importance for the healthcare professional and the healthcare setting. Hospital-based disaster protocols form the cornerstone of disaster response. There is a paucity of data on disaster preparedness training using the virtual tabletop exercise (VTTX) module for interprofessional education from in-hospital and prehospital settings. With the coronavirus disease 2019 (COVID-19) pandemic, we have seen a paradigm shift of education strategies to the virtual realm. Here we attempt to study the impact of an online tabletop exercise workshop on the knowledge and confidence of disaster preparedness among Interprofessional trainees. MATERIAL AND METHODS: Interprofessional trainees from medical, dental, nursing, respiratory therapy, and paramedic domains who consented were included in this study. Institutional ethics committee approval was received and the study was registered with the clinical trials registry India (CTRI), before initiation. The VTTX module has been adapted from the World Health Organization (WHO) COVID-19 training resources. Three international experts from the disaster medicine domain validated the module, questionnaire, and feedback. Wilcoxon signed-rank test was used to compare the parameters (Knowledge and confidence level) pre and post-workshop. RESULTS: A total of 76 candidates with a mean age was 21.67 ± 2.5 (range:19–36) were part of the workshop. Comparison of the median scores and interquartile range of confidence level and knowledge respectively before [38 (29.25–45.75), 9 (7–11)] and after [51.50 (45–60), 11 (10–12)] the workshop showed vital significance (p-value < 0.001). All participants gave positive feedback on the workshop meeting the objectives. The majority agreed that the workshop improved their self-preparedness (90%) and felt that the online platform was appropriate (97.5%). CONCLUSIONS: This study sheds light on the positive impact of the online VTTX based workshop on disaster preparedness training among interprofessional trainees. Disaster preparedness training using available online platforms may be effectively executed with the VICTEr workshop even during the COVID-19 pandemic. The VICTEr workshop serves as a primer for developing online modules for effective pandemic preparedness training in interprofessional education

Cytosine-to-Uracil Deamination by SssI DNA Methyltransferase

The prokaryotic DNA(cytosine-5)methyltransferase M.SssI shares the specificity of eukaryotic DNA methyltransferases (CG) and is an important model and experimental tool in the study of eukaryotic DNA methylation. Previously, M.SssI was shown to be able to catalyze deamination of the target cytosine to uracil if the methyl donor S-adenosyl-methionine (SAM) was missing from the reaction. To test whether this side-activity of the enzyme can be used to distinguish between unmethylated and C5-methylated cytosines in CG dinucleotides, we re-investigated, using a sensitive genetic reversion assay, the cytosine deaminase activity of M.SssI. Confirming previous results we showed that M.SssI can deaminate cytosine to uracil in a slow reaction in the absence of SAM and that the rate of this reaction can be increased by the SAM analogue 5’-amino-5’-deoxyadenosine. We could not detect M.SssI-catalyzed deamination of C5-methylcytosine (m5C). We found conditions where the rate of M.SssI mediated C-to-U deamination was at least 100-fold higher than the rate of m5C-to-T conversion. Although this difference in reactivities suggests that the enzyme could be used to identify C5-methylated cytosines in the epigenetically important CG dinucleotides, the rate of M.SssI mediated cytosine deamination is too low to become an enzymatic alternative to the bisulfite reaction. Amino acid replacements in the presumed SAM binding pocket of M.SssI (F17S and G19D) resulted in greatly reduced methyltransferase activity. The G19D variant showed cytosine deaminase activity in E. coli, at physiological SAM concentrations. Interestingly, the C-to-U deaminase activity was also detectable in an E. coli ung+ host proficient in uracil excision repair

Genetic Affinities of the Central Indian Tribal Populations

Background: The central Indian state Madhya Pradesh is often called as ‘heart of India ’ and has always been an important region functioning as a trinexus belt for three major language families (Indo-European, Dravidian and Austroasiatic). There are less detailed genetic studies on the populations inhabited in this region. Therefore, this study is an attempt for extensive characterization of genetic ancestries of three tribal populations, namely; Bharia, Bhil and Sahariya, inhabiting this region using haploid and diploid DNA markers. Methodology/Principal Findings: Mitochondrial DNA analysis showed high diversity, including some of the older sublineages of M haplogroup and prominent R lineages in all the three tribes. Y-chromosomal biallelic markers revealed high frequency of Austroasiatic-specific M95-O2a haplogroup in Bharia and Sahariya, M82-H1a in Bhil and M17-R1a in Bhil and Sahariya. The results obtained by haploid as well as diploid genetic markers revealed strong genetic affinity of Bharia (a Dravidian speaking tribe) with the Austroasiatic (Munda) group. The gene flow from Austroasiatic group is further confirmed by their Y-STRs haplotype sharing analysis, where we determined their founder haplotype from the North Munda speaking tribe, while, autosomal analysis was largely in concordant with the haploid DNA results. Conclusions/Significance: Bhil exhibited largely Indo-European specific ancestry, while Sahariya and Bharia showed admixed genetic package of Indo-European and Austroasiatic populations. Hence, in a landscape like India, linguistic labe

Mapping development and health effects of cooking with solid fuels in low-income and middle-income countries, 2000–18: a geospatial modelling study

Background: More than 3 billion people do not have access to clean energy and primarily use solid fuels to cook. Use of solid fuels generates household air pollution, which was associated with more than 2 million deaths in 2019. Although local patterns in cooking vary systematically, subnational trends in use of solid fuels have yet to be comprehensively analysed. We estimated the prevalence of solid-fuel use with high spatial resolution to explore subnational inequalities, assess local progress, and assess the effects on health in low-income and middle-income countries (LMICs) without universal access to clean fuels. Methods: We did a geospatial modelling study to map the prevalence of solid-fuel use for cooking at a 5 km × 5 km resolution in 98 LMICs based on 2·1 million household observations of the primary cooking fuel used from 663 population-based household surveys over the years 2000 to 2018. We use observed temporal patterns to forecast household air pollution in 2030 and to assess the probability of attaining the Sustainable Development Goal (SDG) target indicator for clean cooking. We aligned our estimates of household air pollution to geospatial estimates of ambient air pollution to establish the risk transition occurring in LMICs. Finally, we quantified the effect of residual primary solid-fuel use for cooking on child health by doing a counterfactual risk assessment to estimate the proportion of deaths from lower respiratory tract infections in children younger than 5 years that could be associated with household air pollution. Findings: Although primary reliance on solid-fuel use for cooking has declined globally, it remains widespread. 593 million people live in districts where the prevalence of solid-fuel use for cooking exceeds 95%. 66% of people in LMICs live in districts that are not on track to meet the SDG target for universal access to clean energy by 2030. Household air pollution continues to be a major contributor to particulate exposure in LMICs, and rising ambient air pollution is undermining potential gains from reductions in the prevalence of solid-fuel use for cooking in many countries. We estimated that, in 2018, 205 000 (95% uncertainty interval 147 000–257 000) children younger than 5 years died from lower respiratory tract infections that could be attributed to household air pollution. Interpretation: Efforts to accelerate the adoption of clean cooking fuels need to be substantially increased and recalibrated to account for subnational inequalities, because there are substantial opportunities to improve air quality and avert child mortality associated with household air pollution. Funding: Bill & Melinda Gates Foundation

Use of multidimensional item response theory methods for dementia prevalence prediction : an example using the Health and Retirement Survey and the Aging, Demographics, and Memory Study

Background Data sparsity is a major limitation to estimating national and global dementia burden. Surveys with full diagnostic evaluations of dementia prevalence are prohibitively resource-intensive in many settings. However, validation samples from nationally representative surveys allow for the development of algorithms for the prediction of dementia prevalence nationally. Methods Using cognitive testing data and data on functional limitations from Wave A (2001-2003) of the ADAMS study (n = 744) and the 2000 wave of the HRS study (n = 6358) we estimated a two-dimensional item response theory model to calculate cognition and function scores for all individuals over 70. Based on diagnostic information from the formal clinical adjudication in ADAMS, we fit a logistic regression model for the classification of dementia status using cognition and function scores and applied this algorithm to the full HRS sample to calculate dementia prevalence by age and sex. Results Our algorithm had a cross-validated predictive accuracy of 88% (86-90), and an area under the curve of 0.97 (0.97-0.98) in ADAMS. Prevalence was higher in females than males and increased over age, with a prevalence of 4% (3-4) in individuals 70-79, 11% (9-12) in individuals 80-89 years old, and 28% (22-35) in those 90 and older. Conclusions Our model had similar or better accuracy as compared to previously reviewed algorithms for the prediction of dementia prevalence in HRS, while utilizing more flexible methods. These methods could be more easily generalized and utilized to estimate dementia prevalence in other national surveys